Abstract
Vision-based scene recognition and analysis is an emerging field and actively conceded in computer vision and robotics area. Classifying the complex scenes in a real-time environment is a challenging task to solve. In this paper, an indoor and outdoor scene recognition approach by linear combination (fusion) of global descriptor (GIST) and Local Energy based Shape Histogram (LESH) descriptor with Canonical Correlation Analysis (CCA) is proposed. The experiments have been carried out using publicly available 15-dataset and the fused features are modeled by Random forest and K-Nearest Neighbor for classification. In the experimental results, K-NN exhibits the good performance in our proposed approach with an average accuracy rate of 81.62%, which outperforms the random forest classifier.
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References
Quattoni, A., Torralba, A.: Recognizing indoor scenes. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2009), pp. 413–420. IEEE (2009)
Niu, Z., Hua, G., Gao, X., Tian, Q.: Context aware topic model for scene recognition. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2743–2750. IEEE (2012)
Fei-Fei, L., Perona, P.: A Bayesian hierarchical model for learning natural scene categories. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2005), vol. 2, pp. 524–531. IEEE (2005)
Szummer, M., Picard, R.W.: Indoor-outdoor image classification. In: Proceedings of the IEEE International Workshop on Content-Based Access of Image and Video Database, pp. 42–51. IEEE (1998)
Luo, J., Savakis, A.: Indoor vs outdoor classification of consumer photographs using low-level and semantic features. In: Proceedings of the International Conference on Image Processing, vol. 2, pp. 745–748. IEEE (2001)
Kim, W., Park, J., Kim, C.: A novel method for efficient indoor-outdoor image classification. J. Signal Process. Syst. 61(3), 251–258 (2010)
Li, Q., Wu, J., Tu, Z.: Harvesting mid-level visual concepts from large-scale internet images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 851–858 (2013)
Chen, Y., Pan, D., Pan, Y., Liu, S., Gu, A., Wang, M.: Indoor scene understanding via monocular RGB-D images. Inf. Sci. 320, 361–371 (2015)
Shahriari, M., Bergevin, R.: A two-stage outdoor-indoor scene classification framework: experimental study for the outdoor stage. In: International Conference on Digital Image Computing: Techniques and Applications (DICTA), pp. 1–8. IEEE (2016)
Ren, Y., Chen, C., Li, S., Kuo, C.C.J.: GAL: a global-attributes assisted labeling system for outdoor scenes. J. Vis. Commun. Image Represent. 42, 192–206 (2017)
Oliva, A., Torralba, A.: Modeling the shape of the scene: a holistic representation of the spatial envelope. Int. J. Comput. Vis. 42(3), 145–175 (2001)
Lazebnik, S., Schmid, C., Ponce, J.: Beyond bags of features: spatial pyramid matching for recognizing natural scene categories. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 2169–2178. IEEE (2006)
Wajid, S.K., Hussain, A.: Local energy-based shape histogram feature extraction technique for breast cancer diagnosis. Expert Syst. Appl. 42(20), 6990–6999 (2015)
Thompson, B.: Canonical correlation analysis. In: Encyclopedia of Statistics in Behavioral Science (2005)
Liaw, A., Wiener, M., et al.: Classification and regression by randomForest. R News 2(3), 18–22 (2002)
Wu, X., Kumar, V., Quinlan, J.R., Ghosh, J., Yang, Q., Motoda, H., McLachlan, G.J., Ng, A., Liu, B., Philip, S.Y., et al.: Top 10 algorithms in data mining. Knowl. Inf. Syst. 14(1), 1–37 (2008)
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Arunnehru, J., Yashwanth, A., Shammer, S. (2018). Canonical Correlation-Based Feature Fusion Approach for Scene Classification. In: Abraham, A., Muhuri, P., Muda, A., Gandhi, N. (eds) Intelligent Systems Design and Applications. ISDA 2017. Advances in Intelligent Systems and Computing, vol 736. Springer, Cham. https://doi.org/10.1007/978-3-319-76348-4_14
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DOI: https://doi.org/10.1007/978-3-319-76348-4_14
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